Cross‐modal integration of bulk RNA‐seq and single‐cell RNA sequencing data to reveal T‐cell exhaustion in colorectal cancer

Abstract

Colorectal cancer (CRC) is a relatively common malignancy clinically and the second leading cause of cancer‐related deaths. Recent studies have identified T‐cell exhaustion as playing a crucial role in the pathogenesis of CRC. A long‐standing challenge in the clinical management of CRC is to understand how T cells function during its progression and metastasis, and whether potential therapeutic targets for CRC treatment can be predicted through T cells. Here, we propose DeepTEX, a multi‐omics deep learning approach that integrates cross‐model data to investigate the heterogeneity of T‐cell exhaustion in CRC. DeepTEX uses a domain adaptation model to align the data distributions from two different modalities and applies a cross‐modal knowledge distillation model to predict the heterogeneity of T‐cell exhaustion across diverse patients, identifying key functional pathways and genes. DeepTEX offers valuable insights into the application of deep learning in multi‐omics, providing crucial data for exploring the stages of T‐cell exhaustion associated with CRC and relevant therapeutic targets.